The proposed symmetric network architecture is shown in Fig. REF , based on the framework of DeepCrack[1]} with the same structural parameters such as the kernel size and the number of channels. The encoder consists of 5 convolutional blocks with 13 convolutional layers. Each convolutional layer contains a standard size-invariant convolution operation for the abstraction of crack features. With the combination of several convolutional layers, each feature map of the current scale is created at the end of each block. For downsampling, a max-pooling layer after each block compresses the image into quarter-size, preserving the values and indices of the local maxima. The shrinkage of the image consequently yields an increase in the size of the receptive field (RF) for the following layer, leading to a sparser feature map in the next block.
